Yesterday, everyone was looking at Google releasing yet another new model. Today, what’s worth a closer look is this: which benchmarks is Gemini 4 Argon genuinely strong on, and where is it just spin?
Let’s start with the strengths. On the long-horizon software engineering benchmark DeepSWE, it scored 77.9%, beating Claude Opus 5.5 at 74.2% and GPT-6 Astra at 74.1%. It took the top spot for the first time on the Vals Index, which tests knowledge work in finance, law, and tax, with a score of 68.9%. It scored 51.3% on AutomationBench for enterprise workflow automation, nearly 9 points ahead of the runner-up. It got 91.9% on Vibe Code Bench, 84.2% on GraphWalks for extremely long text (256,000 to 1 million tokens), and 91.7% on long-video understanding. The overall index from third party Artificial Analysis gave it 53 points, tying GPT-6 Astra and beating Google’s previous flagship by 23 points.
The weaknesses need mentioning too. It scored only 57.4% on Terminal Bench for hands-on terminal tasks, trailing GPT-6 Astra and Opus 5.5. Its 19.6% on Harvey, a legal-agent benchmark, was nearly three times the runner-up’s score, but still low in absolute terms. And since these are mostly official self-reported results, while competitors’ scores come from public leaderboards, take the numbers with a grain of salt.
The pricing is seriously aggressive. Promotional pricing is $2 per million input tokens and $10 per million output tokens, with a further 5% discount on cached inputs. Prices double after November 30. Paying subscribers can already use it in the Gemini app, AI Mode, and Antigravity.
Real-world evidence says more than scores. In Google’s overnight internal coding test, 2 optimizations were merged and 4 proposals were rejected—not everything got automatically approved. A Chrome engineer used it to complete an architectural refactor spanning hundreds of files in the 40-million-line Chromium codebase. Early enterprise customers include Deloitte, LexisNexis, and Isomorphic Labs.
From a crypto perspective, there are two threads to watch. The Fairwind plan is to give defenders a version with cybersecurity guardrails removed. Yesterday’s case of an OpenAI engineer being arrested was AI catching AI; today’s story is AI defending against AI. Both offense and defense are stepping up. And for an agent economy to really take off, an on-chain settlement layer—gas, identity, and payments—is foundational infrastructure that can’t be avoided. That’s where crypto has its most tangible role in the AI narrative.
Finally, the question that really matters: however impressive benchmark scores may look, they’re only an entry ticket. What enterprises really care about is pricing, rate limits, and data governance terms. Google still hasn’t handed in its commercialization report card.
#GoogleReleasesGemini4ArgonAIModel
#AI #Gemini
This is not investment advice. DYOR; trading involves risk.
Let’s start with the strengths. On the long-horizon software engineering benchmark DeepSWE, it scored 77.9%, beating Claude Opus 5.5 at 74.2% and GPT-6 Astra at 74.1%. It took the top spot for the first time on the Vals Index, which tests knowledge work in finance, law, and tax, with a score of 68.9%. It scored 51.3% on AutomationBench for enterprise workflow automation, nearly 9 points ahead of the runner-up. It got 91.9% on Vibe Code Bench, 84.2% on GraphWalks for extremely long text (256,000 to 1 million tokens), and 91.7% on long-video understanding. The overall index from third party Artificial Analysis gave it 53 points, tying GPT-6 Astra and beating Google’s previous flagship by 23 points.
The weaknesses need mentioning too. It scored only 57.4% on Terminal Bench for hands-on terminal tasks, trailing GPT-6 Astra and Opus 5.5. Its 19.6% on Harvey, a legal-agent benchmark, was nearly three times the runner-up’s score, but still low in absolute terms. And since these are mostly official self-reported results, while competitors’ scores come from public leaderboards, take the numbers with a grain of salt.
The pricing is seriously aggressive. Promotional pricing is $2 per million input tokens and $10 per million output tokens, with a further 5% discount on cached inputs. Prices double after November 30. Paying subscribers can already use it in the Gemini app, AI Mode, and Antigravity.
Real-world evidence says more than scores. In Google’s overnight internal coding test, 2 optimizations were merged and 4 proposals were rejected—not everything got automatically approved. A Chrome engineer used it to complete an architectural refactor spanning hundreds of files in the 40-million-line Chromium codebase. Early enterprise customers include Deloitte, LexisNexis, and Isomorphic Labs.
From a crypto perspective, there are two threads to watch. The Fairwind plan is to give defenders a version with cybersecurity guardrails removed. Yesterday’s case of an OpenAI engineer being arrested was AI catching AI; today’s story is AI defending against AI. Both offense and defense are stepping up. And for an agent economy to really take off, an on-chain settlement layer—gas, identity, and payments—is foundational infrastructure that can’t be avoided. That’s where crypto has its most tangible role in the AI narrative.
Finally, the question that really matters: however impressive benchmark scores may look, they’re only an entry ticket. What enterprises really care about is pricing, rate limits, and data governance terms. Google still hasn’t handed in its commercialization report card.
#GoogleReleasesGemini4ArgonAIModel
#AI #Gemini
This is not investment advice. DYOR; trading involves risk.